Only trusted tech partnerships can hasten insurance AI innovation
Insurtechs must spearhead honest discussions about the risks of delaying GenAI adoption.
The insurance industry often faces criticism for its reluctance to adopt new technologies. But now, Generative AI (GenAI) offers an unprecedented opportunity to streamline and automate everything from underwriting to claims processing.
Despite this, a significant challenge remains: persuading insurers and agents to trust this potent new technology.
Insurtechs, positioned at the intersection of innovation and tradition, have a vital role in fostering a collaborative GenAI future, but it all must be anchored in trust and transparency.
Related: MGAs and AI: The case for more optimism than caution
Insurtechs must spearhead honest discussions about the risks of delaying GenAI adoption. While legacy systems are familiar and deeply ingrained, they are also costly to maintain and hinder innovation.
Picture a scenario where underwriters spend countless hours sifting through data mountains to assess risk, only to be overtaken by competitors utilizing AI-powered systems that produce nuanced risk profiles in mere moments. This is not a distant future but the current reality confronting the insurance industry.
Why should insurers leave behind reliable systems for the unknown terrain of AI?
The answer lies in the inherent limitations of the old guard. Maintaining these systems continually drains resources, and security vulnerabilities are increasingly problematic. Legacy systems are becoming harder and more expensive to secure. More recently, cloud- and digital-native AI solutions can provide a way forward, seamlessly integrating with existing infrastructures, thus overcoming legacy system limitations and heralding a new era of efficiency and security.
By vividly illustrating these risks and presenting a compelling vision of AI’s benefits, insurtechs can motivate early adoption and prevent insurers from falling behind.
Cultivating trust
Building trust involves more than showcasing potential advantages. Insurtechs must demonstrate unwavering commitment to robust security, strict compliance, and transparent communication.
Insurtechs must candidly communicate their capabilities and limitations, highlighting their expertise through a proven track record in AI innovation and ethical adherence. This approach nurtures a partnership mindset, assuring insurers they are not relinquishing control to an opaque system.
As AI integration becomes more widespread, prioritizing compliance, data privacy, and bias mitigation is essential, underpinned by strong governance structures ensuring AI operates within ethical norms. Alignment is vital here; ensuring that an artificial intelligence system’s goals and behaviors are aligned with human values and intentions. As AI systems become increasingly sophisticated and autonomous, it becomes imperative to guarantee that their actions remain beneficial to humans.
Misalignment can occur when AI systems optimize for objectives that, while aligned with their programmed goals, might diverge from the nuanced and complex ethical standards that govern human decision-making. For instance, an AI designed to maximize efficiency in claims processing might inadvertently deny valid claims to cut costs, neglecting the ethical obligation to treat policyholders fairly.
Ongoing monitoring and auditing are crucial to ensure that AI behaviors remain consistent with human values and decisions over time, especially as these systems encounter new and unforeseen situations. This continuous oversight can help mitigate risks such as bias, discrimination, and unintended harmful outcomes, ensuring that AI systems act in ways that are both predictable and aligned with the broader goals of their human operators. Insurers must develop actionable processes that marry AI with ethical and regulatory guidelines.
Huge potential benefits
The advantages of AI are manifold and translate into concrete benefits for insurers, MGAs, and agents. One of the key drivers is cost reduction. Automating tedious and error-prone tasks like data entry and claims processing frees up valuable human resources for higher-value activities. Imagine an AI-powered system like ChatGPT assisting underwriters by locating and summarizing essential documents, pinpointing key information, and streamlining workflows. This not only reduces processing time but also allows experienced underwriters to concentrate on complex cases, leveraging their expertise where it truly matters.
GenAI also significantly enhances risk assessment accuracy, leading to better pricing and reduced fraud. AI-driven analysis surpasses traditional methods, interpreting data with exceptional precision and creating detailed risk profiles. This leads to more informed decisions, quicker policy issuance, and heightened customer satisfaction. Carriers, MGAs, and sizable agencies throughout the industry have already embraced AI to automate repetitive aspects of their workload and reprioritize this time into more meaningful functions. Further, AI is creating efficiencies across claims processing, fraud detection, customer service, and reduction in expense ratios.
An additional crucial advantage is the transparency provided by GenAI, especially when employing methods such as Retrieval-Augmented Generation (RAG). This enables the clarification of the reasoning behind its decisions, unlike conventional opaque models. These capabilities minimize inaccuracies and strengthen confidence in AI-generated materials. In the realm of underwriting, RAG can guarantee precise policyholder information and regulatory conformity, thereby enhancing trust among insurers and regulatory bodies alike.
Strategic steps
Collaborating with credible AI vendors within the insurtech landscape is imperative to successfully harnessing the power of this innovative technology. Insurers should seek partners with credentials and tangible success with AI innovation, and that have scalable solutions that can grow with your book of business. Developing an ecosystem of trusted collaborators and partnerships maximizes operations and enables real, tangible results.
Incorporating GenAI successfully should be led by identifying use cases and pilots. A phased deployment strategy effectively manages risk, starting with pilot programs and gradually extending AI tool usage. Building robust cloud-based infrastructure ensures scalable compute power and storage, facilitating heavy data processing.
Assessing AI performance is essential to ensure decisions are accurate and unbiased. Implementing key performance indicators (KPIs) relevant to underwriting allows for effective model calibration, aligning AI models with business goals and industry standards. Continuous improvement positions underwriting departments to harness AI’s transformative potential responsibly and effectively.
Leandro DalleMule is global head of Insurance and general manager at Planck, an AI-based risk research and data solutions company for commercial insurers and producers and a subsidiary of Applied Systems. Opinions expressed here are the author’s own.
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